Zikang Yan

The rich information and complex background of industrial images make it a challenging task to improve the high compression rate of images. Current learning-based image compression methods mostly use customized convolutional neural networks (CNNs), which find it difficult to cope with the complex production background of industrial images. This causes useful information to be lost in the abundance of irrelevant data, making it difficult to accurately extract important features during the feature extraction stage. To address this, a Multi-path Residual Asymmetric Convolutional Compression Network (MRACNN) is proposed. Firstly, a Multi-path Residual Asymmetric Convolution Block (MRACB) is introduced, which includes the Multi-path Residual Asymmetric Convolution Down-sampling Module for down-sampling in the encoder to extract key features, and the Mult-path Residual Asymmetric Convolution Up-sampling Module for up-sampling in the decoder to recover details and reconstruct the image. This feature transfer and information flow enables the better capture of image details and important information, thereby improving the quality and efficiency of image compression and decompression. Furthermore, a two-branch enhanced local attention mechanisms, and a channel-squeezing entropy model based on the compression-based enhanced local attention module is proposed to enhance the performance of the modeled compression. Extensive experimental evaluations demonstrate that the proposed method outperforms state-of-the-art techniques, achieves superior Rate–Distortion Performance, and excels in preserving local details.